Method and System for Modeling Behavior of a Visitor to An E-Commerce Location

Abstract

A method for modeling the behavior of a visitor to an e-commerce location includes the steps of automatically obtaining one or more visitor characteristic values, and automatically developing a model of the visitor's behavior according to a nonlinear state estimation technique (NSET). The method also includes then automatically estimating a set of visitor behavior characteristic values with said model that model said visitor's behavior.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application is a continuation application of U.S. patent application Ser. No. 11 / 846,211, filed Aug. 28, 2007 which is a divisional application of U.S. patent application Ser. No. 09 / 561,238, filed Apr. 28, 2000, and issued as U.S. Pat. No. 7,386,426, which claims the benefit under 35. U.S.C. §119(e) of Provisional Patent Application No. 60 / 131,898, filed Apr. 30, 1999, all of which are hereby incorporated herein for all purposes.TECHNICAL FIELD OF THE INVENTION[0002]This invention relates generally to methods and systems for process modeling and analysis. More particularly, the present invention relates to an improved nonlinear state estimation technique (NSET) to perform process modeling and analysis of, for example, buyer purchasing characteristics.BACKGROUND OP THE INVENTION[0003]Systems and methods for process modeling and analysis are well known in the art. For example, artificial neural networks, such as those disclosed in co-...

AI Technical Summary

Benefits of technology

[0007]Therefore, a need exists for a method and system for nonlinear state estimation for process modeling and analysis that can achieve improved matrix conditioning, improved model stability and improved prediction accuracy. Such a method and system would use regularization principles in the inversion of prototype matrices, so as to avoid prior art problems encountered in the inversion of troublesome singular or near-singular prototype matrices.

[0008]An ever further need exists for an NSET with improved stability in the selection of datasets included in the prototype matrix that can reduce or eliminate co-linearities among the prototypical data points.

[0009]A still further need exists for an NSET method and system that uses a distance / similarity function optimized to provide greater accuracy and modeling flexibility.

Problems solved by technology

Bach of these prior art methods, however, suffers from limitations in terms of accuracy and modeling flexibility.

Artificial neural networks, for example, although suitable for modeling certain systems, require extensive training and are time-intensive, which makes them unsuitable for applications in which a system, and corresponding modeling of that system, must be done in near real time.

An artificial neural network would thus be unsuitable, for example, to predict behavior in a e-commerce setting where the future behavior of a customer is desired to be known.

Applying artificial neural networks to model the behavior of each customer in such an application, in which new information (in the form of additional variables) becomes available as time evolves, is not possible, as means do not exist for rapid adjustment of the model of such a system to predict behavior.

The iterative process required to train an artificial neural network is not conducive to modeling rapidly changing systems in which a rapid model adjustment is necessary once one or more new variables have become available.

MSETs and basic NSETs also face limitations in that they rely upon the inversion of data matrices (recognition matrices) that are sometimes singular (in which case inversion is impossible) or near-singular, in which case inversion is possible but end result prediction accuracy is negatively affected.

Furthermore, MSETs have poor stability with respect to choice of data included in the prototype matrix, i.e., the inclusion / exclusion of any particular single data point in the prototype matrix can unduly affect prediction results.

Such a distance / similarity function, while generally providing better-conditioned recognition matrices, is not optimal in terms of accuracy and modeling flexibility.

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